Graph neural networks (GNNs) [1]}, [2]}, [3]} are widely adopted in various domains, such as social media mining [4]}, knowledge graph [5]} and recommender system [6]}, due to remarkable performance in learning representations. Graph learning, a topic with growing popularity, aims to learn node representation containing both topological and attribute information in a given graph. Despite the outstanding performance in various tasks, GNNs still inherit or even amplify societal bias from input graph data [7]}.
The biased node representation largely limits the application of GNNs in many high-stake tasks, such as job hunting [8]} and crime ratio prediction [9]}. Hence, bias mitigation that facilitates the research on fair GNNs is in an urgent need.
